Matej
10/05/2023, 6:49 PMhorizon = 12
n_windows = len(df_test) - horizon
# Perform cross-validation without refitting
cv_results = fcst.cross_validation(
df_test,
# static_features=[],
n_windows=n_windows,
h=horizon,
step_size=1,
refit=False,
fitted=True
)
With the above setup however I get:
--> 462 self.fit_models(X, y)
463 if fitted:
464 fitted_values = self._compute_fitted_values(
465 X_with_info=X_with_info,
...
--> 677 raise ValueError('Input data must be 2 dimensional and non empty.')
679 # determine feature names
680 if feature_name == 'auto':
ValueError: Input data must be 2 dimensional and non empty.
Is there something I am not getting ? : )
Thanks for patience.José Morales
10/05/2023, 6:55 PMMatej
10/05/2023, 6:55 PMcv_results = fcst.cross_validation(
df_test,
static_features=[],
n_windows=2,
h=horizon,
step_size=1,
refit=False,
fitted=True
)
but id like to simply go over the entire dataset, somehow I am unable to set up n_windows correctlyJosé Morales
10/05/2023, 6:58 PMstatic_features
argument. By providing an empty list you're saying none are static (they all change over time) which is probably causing a bad join somewhereMatej
10/05/2023, 6:59 PMJosé Morales
10/05/2023, 7:01 PMMatej
10/05/2023, 7:02 PMJosé Morales
10/05/2023, 7:03 PMMatej
10/05/2023, 7:04 PMJosé Morales
10/05/2023, 7:09 PMinput_size
but you have to be careful with the number of samples there, because if you're using lag5 for example and dropna=True
then the features drop 5 rows, so you'll need input_size=6
to get a single sample at the endMatej
10/05/2023, 7:29 PMmax_horizon: int, optional (default=None)
Train this many models, where each model will predict a specific horizon.
• it seems this is for something else. For when I assume different dynamics for each time horizon it seems.before_predict_callback
after_predict_callback
José Morales
10/05/2023, 7:46 PMMatej
10/05/2023, 7:50 PM